A Random Forest Approach to Identifying Young Stellar Object Candidates in the Lupus Star-Forming Region

Abstract

The identification and characterization of stellar members within a star-forming region are critical to many aspects of star formation, including formalization of the initial mass function, circumstellar disk evolution and star-formation history. Previous surveys of the Lupus star-forming region have identified members through infrared excess and accretion signatures. We use machine learning to identify new candidate members of Lupus based on surveys from two space-based observatories: ESA's Gaia and NASA's Spitzer. Astrometric measurements from Gaia's Data Release 2 and astrometric and photometric data from the Infrared Array Camera (IRAC) on the Spitzer Space Telescope, as well as from other surveys, are compiled into a catalog for the Random Forest (RF) classifier. The RF classifiers are tested to find the best features, membership list, non-membership identification scheme, imputation method, training set class weighting and method of dealing with class imbalance within the data. We list 27 candidate members of the Lupus star-forming region for spectroscopic follow-up. Most of the candidates lie in Clouds V and VI, where only one confirmed member of Lupus was previously known. These clouds likely represent a slightly older population of star-formation.

31 pages, 14 figures

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• Lupus DANCe. Census of stars and 6D structure with Gaia-DR2 data
• A Gaia Survey for Young Stars Associated with the Lupus Clouds
• The eROSITA Final Equatorial-Depth Survey (eFEDS). The stellar counterparts of eROSITA sources identified by machine learning and Bayesian algorithms
• Stellar population of the Rosette Nebula and NGC 2244: application of the probabilistic random forest
• A wide survey for circumstellar disks in the Lupus complex
• Search for Brown Dwarfs in IC 1396 with Subaru HSC: Interpreting the Impact of Environmental Factors on Sub-stellar Population
• Disks in Nearby Young Stellar Associations Found Via Virtual Reality